Did Twitter’s Removal of Government and State-Affiliated Media Labels Expand the Influence of State Actors?


Allison Koh



PaCSS 2023 APSA Pre-Conference
August 30, 2023

#TBT

“Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda. We believe that people have the right to know when a media account is affiliated directly or indirectly with a state actor” (Twitter, 2020).

Sources: Internet Archive, Twitter

Twitter: A safe space for state influence?


State Actors on Twitter

  • Russian interference in the US 2016 election1
  • Chinese influence operations and Xinjiang2
  • Iranian involvement in anti-Saudi discourse3

Effect of Twitter’s changes

  • State actor labels ⬇️ engagement4
  • Tweet engagement in far-right networks ⬆️ since October 20225

Twitter: A safe space for state influence?

 

 

Source: Rest of World, Lumen Database

Twitter’s Policy Changes in April 2023




March 29: ⬆️ engagement with prominent state media from China, Russia, Iran (Kann 2023)

April 6: “In the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their Tweets with these labels to people.”

April 12: NPR quits Twitter after being labeled as “state-affiliated media” (Folkenflik 2023)

April 21: Twitter removes all labels for “government” and “state-affiliated media”

Sources: DFRLab, Twitter, Internet Archive

Research Questions and Definitions


After Twitter’s removal of profile labels on April 21st, 2023…

  • …did engagement with state actors increase?
  • …did state actors change how they use the platform?


🔎📕 Definitions (Schafer 2019; Twitter 2020)

  • State actor: Any individual or entity connected to government or state-affiliated media
  • Government official: Key individuals/entities representing “voices of the nation state abroad”
  • State-affiliated media: Outlets where the state exercises control over editorial content

Hypotheses and Exploratory Analysis 🔎

After Twitter officially removed labels from state actors’ accounts…

  • Hypothesis 1: …engagement with state actors ⬆️
  • Hypothesis 2: …state actors changed their behavior on the platform.
  • Sub-hypotheses comparing state actor types 🏦📺
    • Official government accounts as voices of the state ➡️ less responsive to platform changes
    • Limited cues on editorial independence of state media ➡️ opportunities for manipulation
    • State-affiliated media will be more affected by this platform change.


Exploratory Analysis 🌏

  • Differences between state actors from China, Iran, and Russia

Data

State Actors: Hamilton 2.0 Dashboard (Schafer 2019)


1,177 accounts linked to Chinese, Iranian, and Russian state actors

Proxy for accounts that had government and state-affiliated media labels

Data

Tweets: Twitter Academic API via twarc CLI

Tweet timeline endpoint; up to ~3,200 tweets per account

Successfully collected data from 1,038 timelines

Measurement


Dependent variables

Daily aggregated volumes 📈

  • Retweets and likes of tweets authored by state actors (H1)
  • Tweet volumes of posts produced by state actors (H2)


Independent variables

  • State actor type (official government or state-affiliated media)
  • Time-related variables for parametric tests

Results

Placebo Tests—Engagement and Tweet Volume

Results

Disaggregated Analyses

Retweets





RTs of media accounts ⬆️


RTs of Russian accounts ⬆️

Results

Disaggregated Analyses

Likes





⬆️ likes of Russian accounts compared to Chinese/Iranian accounts

Results

Disaggregated Analyses

Tweets by state actors





⬆️ tweets by media accounts

⬆️ tweets by Russian and Chinese accounts

Results

Summary


  • The influence of state-affiliated media accounts significantly increased in the month after source labels were removed from all state actors’ accounts.
  • State actors from Russia also benefited from this change in platform policy, and posted significantly more content in the month after their source labels were removed.
  • Chinese accounts tweeted more, but observed no significant increases in engagement.
  • Official government and Iranian users did not significantly expand their reach.

Conclusion


  • By removing “government” and “state-affiliated media” labels, Twitter has increased the space available for digital authoritarians to exert influence on the platform.
  • This research illustrates the importance of comparing digital authoritarians’ social media activity across actor types and geopolitical contexts.
  • My findings also have broader implications for understanding how changes in platform infrastructure can expand the reach of contentious political actors.

Thank you!

koh@hertie-school.org

https://allisonkoh.github.io/

🟦 @allisonwkoh

References

Aguerri, Jesús, Mario Santisteban, and Fernando Miró-Llinares. 2022. “The Fight Against Disinformation and Its Consequences: Measuring the Impact of ‘Russia State-Affiliated Media’ on Twitter.” Preprint. SocArXiv. https://doi.org/10.31235/osf.io/b4qxt.
Barrie, Christopher. 2022. “Did the Musk Takeover Boost Contentious Actors on Twitter?” arXiv Preprint arXiv:2212.10646. https://arxiv.org/abs/2212.10646.
DiResta, Renée, Josh A Goldstein, Carly Miller, and Harvey Wang. 2021. “One Topic, Two Networks: Evaluating Two Chinese Influence Operations on Twitter Related to Xinjiang.” Stanford Internet Observatory, December, 44.
Folkenflik, David. 2023. “NPR Quits Twitter After Being Falsely Labeled as ’State-Affiliated Media’.” NPR, April.
Golovchenko, Yevgeniy, Cody Buntain, Gregory Eady, Megan A. Brown, and Joshua A. Tucker. 2020. “Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube During the 2016 U.S. Presidential Election.” The International Journal of Press/Politics 25 (3): 357–89. https://doi.org/10.1177/1940161220912682.
Kann, Alyssa. 2023. “State-Controlled Media Experience Sudden Twitter Gains After Unannounced Platform Policy Change.” DFRLab.
Kießling, Bastian, Jan Homburg, Tanja Drozdzynski, and Steffen Burkhardt. 2020. “State Propaganda on Twitter.” In Disinformation in Open Online Media, edited by Christian Grimme, Mike Preuss, Frank W. Takes, and Annie Waldherr, 182–97. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-39627-5_14.
Liang, Fan, Qinfeng Zhu, and Gabriel Miao Li. 2022. “The Effects of Flagging Propaganda Sources on News Sharing: Quasi-Experimental Evidence from Twitter.” The International Journal of Press/Politics, March, 194016122210869. https://doi.org/10.1177/19401612221086905.
Romm, Tony. 2018. “Iranians Masqueraded as Foreign Journalists to Push Political Messages Online, New Twitter Data Shows.” Washington Post, October.
Ross, Andrew RN, Cristian Vaccari, and Andrew Chadwick. 2022. “Russian Meddling in US Elections: How News of Disinformation’s Impact Can Affect Trust in Electoral Outcomes and Satisfaction with Democracy.” Mass Communication and Society 25 (6): 786–811.
Schafer, Bret. 2019. “Hamilton 2.0 Methodology & FAQs.” Alliance For Securing Democracy.
Twitter. 2020. “New Labels for Government and State-Affiliated Media Accounts.” https://blog.twitter.com/en_us/topics/product/2020/new-labels-for-government-and-state-affiliated-media-accounts.